With the rapid growth in network services, it becomes more and more important for network operators or service providers to monitor network performance or service quality, so as to detect any abnormal network behavior or service degradation and respond to it as quickly as possible.
As an example, Key Performance Indicator (KPI) data is used as a measure of network performance or service quality provided by a network. The KPI data is known as a category of time-series data. Conventionally, off-line statistical analysis methods are applied to time-series data, i.e., the time-series data needs to be stored first and then compared with one or more defined thresholds, so as to find an anomaly. However, there is typically a huge amount of KPI data that is generated at a very high speed. In this case, a large storage capacity is needed for storing the KPI data to be analyzed. Moreover, many manual operations are required to define and adjust the thresholds. However, for the KPI data, such manual operations become impractical as the number of network elements increases. It is thus a challenge to detect anomalies in the KPI data in a timely manner.
WO 2009/008783A1 discloses a method for measuring a performance indicator with reduced complexity. In the method, a performance filter is applied to a raw data and the performance indicator is calculated from the filtered data. In this way, it is possible to simultaneously measure a plurality of different performance indicators, by applying different performance filters and corresponding algorithms on the same raw data.
However, there is still a need for an improved solution for detecting abnormal network behavior or service degradation from the performance indicator data.